Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion with...Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.展开更多
In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches d...In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.展开更多
Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.T...Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.This area is characterized by adverse geological conditions such as rock piles,debris slopes and unstable slopes.Furthermore,due to the absence of historical rainfall records and landslide inventories,empirical methods are not applicable for the analysis of rainfall-induced landslides.Thus we employ a physically based landslide susceptibility analysis model by using highprecision unmanned aerial vehicle(UAV)photogrammetry,field boreholes and long short term memory(LSTM)neural network to obtain regional topography,soil properties,and rainfall parameters.We applied the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability(TRIGRS)model to simulate the distribution of shallow landslides and variations in porewater pressure across the region under different rainfall intensities and three rainfall patterns(advanced,uniform,and delayed).The landslides caused by advanced rainfall pattern mostly occurred in the first 12 hours,but the landslides caused by delayed rainfall pattern mostly occurred in the last 12 hours.However,all the three rainfall patterns yielded landslide susceptibility zones categorized as high(1.16%),medium(8.06%),and low(90.78%).Furthermore,total precipitation with a rainfall intensity of 35 mm/h for 1 hour was less than that with a rainfall intensity of 1.775 mm/h for 24hours,but the areas with high and medium susceptibility increased by 3.1%.This study combines UAV photogrammetry and LSTM neural networks to obtain more accurate input data for the TRIGRS model,offering an effective approach for predicting rainfall-induced shallow landslides in regions lacking historical rainfall records and landslide inventories.展开更多
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It ...This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.展开更多
建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然...建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然后采用Canopy算法结合K-means聚类算法将深圳市地铁站进行聚类分析,以此发掘不同类型站点骑行规律;最后在此基础上建立了NGO-LSTM预测模型对站点的需求量进行预测分析,并与其他模型进行对比。实验结果表明,NGO-LSTM模型的决定系数达到0.90。展开更多
文摘Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.
基金This work is supported by‘The Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201714)’‘Weihai Science and Technology Development Program(2016DXGJMS15)’‘Key Research and Development Program in Shandong Provincial(2017GGX90103)’.
文摘In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time.
基金the National Natural Science Foundation of China(No.51878668)the Natural Science Foundation of Hunan Province(No.2021JJ10063)the Fundamental Research Funds for the Central Universities of Central South University(Nos.2020zzts167,2020zzts154,2019zzts009)。
文摘Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.This area is characterized by adverse geological conditions such as rock piles,debris slopes and unstable slopes.Furthermore,due to the absence of historical rainfall records and landslide inventories,empirical methods are not applicable for the analysis of rainfall-induced landslides.Thus we employ a physically based landslide susceptibility analysis model by using highprecision unmanned aerial vehicle(UAV)photogrammetry,field boreholes and long short term memory(LSTM)neural network to obtain regional topography,soil properties,and rainfall parameters.We applied the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability(TRIGRS)model to simulate the distribution of shallow landslides and variations in porewater pressure across the region under different rainfall intensities and three rainfall patterns(advanced,uniform,and delayed).The landslides caused by advanced rainfall pattern mostly occurred in the first 12 hours,but the landslides caused by delayed rainfall pattern mostly occurred in the last 12 hours.However,all the three rainfall patterns yielded landslide susceptibility zones categorized as high(1.16%),medium(8.06%),and low(90.78%).Furthermore,total precipitation with a rainfall intensity of 35 mm/h for 1 hour was less than that with a rainfall intensity of 1.775 mm/h for 24hours,but the areas with high and medium susceptibility increased by 3.1%.This study combines UAV photogrammetry and LSTM neural networks to obtain more accurate input data for the TRIGRS model,offering an effective approach for predicting rainfall-induced shallow landslides in regions lacking historical rainfall records and landslide inventories.
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the Project Number“NBU-FFR-2024-1092-04”.
文摘This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.
文摘建立了北方苍鹰算法优化长短期记忆神经网络(northern goshawk optimization-long short term memory,NGO-LSTM)的预测模型。以深圳市共享单车为例,首先对共享单车数据进行预处理,以Geohash算法为基础将骑行的时变数据作为特征输入;然后采用Canopy算法结合K-means聚类算法将深圳市地铁站进行聚类分析,以此发掘不同类型站点骑行规律;最后在此基础上建立了NGO-LSTM预测模型对站点的需求量进行预测分析,并与其他模型进行对比。实验结果表明,NGO-LSTM模型的决定系数达到0.90。
